Searching for New Biomarkers of Renal Diseases through Proteomics
Bibliographic record
Abstract
BACKGROUND: Technological advances have resulted in a renaissance of proteomic studies directed at finding markers of disease progression, diagnosis, or responsiveness to therapy. Renal diseases are ideally suited for such research, given that urine is an easily accessible biofluid and its protein content is derived mainly from the kidney. Current renal prognostic markers have limited value, and renal biopsy remains the sole method for establishing a diagnosis. Mass spectrometry instruments, which can detect thousands of proteins at nanomolar (or even femtomolar) concentrations, may be expected to allow the discovery of improved markers of progression, diagnosis, or treatment responsiveness. CONTENT: In this review we describe the strengths and limitations of proteomic methods and the drawbacks of existing biomarkers, and provide an overview of opportunities in the field. We also highlight several proteomic studies of biomarkers of renal diseases selected from the plethora of studies performed. SUMMARY: It is clear that the field of proteomics has not yet fulfilled its promise. However, ongoing efforts to standardize sample collection and preparation, improve study designs, perform multicenter validations, and create joint industry-regulatory bodies offer promise for the recognition of novel molecules that could change clinical nephrology forever.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".